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Frédéric Gava

Design ofDepartmental

Metacomputing ML

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Goal

User

Computing Unit

Storage

Computing Unit

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Bulk Synchronous Parallelism +

Functional Programming = BSML

Advantages of the BSP model:1. Portability2. Scalability, deadlock free

3. Simple cost model Performance prediction

Advantages of functional programming:

1. High level features (higher order functions, pattern-matching, concrete types, etc…)

2. Savety of the environment3. Programs Proofs

4

The BSP model

T(s) = (max0i<p wi) + hg + L

0 1 2 3 p-1Proc.

5

The BSML language

Library for the « Objective Caml » language (called BSMLlib)

4 Operations on a parallel data structure called parallel vector

Operations to access to the BSP parameters

mkpar : create a parallel vector

apply : parallel point-wise application

put : send values

at : projection of a value

Confluent language : deterministic algorithms

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Problems with BSP

No hierarchical network capacities

Global synchronization is too expensive

No heterogenelity

7

The MP model

Local computing

Communication

No super-steps but m-steps

Proc. 0 1 2 3

MP = messages passing

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New model: DMM We have :

• P BSP units• L, latency of the global network• G, time to send one word

List of nb of processes :

List of times for a barrier :

List of times for a 1-relation :

We have d-steps of super-steps

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New communicationG et L

g et l

BSP Unit jBSP Unit i

j jgi et li

Intranet

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New communicationUnité jUnit i

gi et li

New language: DMML Library for the « Objective Caml » language

Operations to access to the DMM parameters

Operations on departmental vectors

mkdpt : creation of a departmental vector

applydpt : departmental point-wise application

get : received values

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Conclusion BSML = BSP + ML

New model for departmental meta-computing

New language called DMML

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Future Work Polymorphic type system for the language

Implementation of « big » applications

Static Dependent Costs for Estimating Execution Time

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